
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase    :   int						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase	,		__lowercase):




	'''simple docstring'''




	_A								=	'maskformer-swin'

	_A								=	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(   self						:Any	,  a						:Dict=2_2_4	,  a						:int=4	,  a						:Any=3	,  a						:Union[str, Any]=9_6	,  a						:Dict=[2, 2, 6, 2]	,  a						:List[Any]=[3, 6, 1_2, 2_4]	,  a						:Union[str, Any]=7	,  a						:List[str]=4.0	,  a						:List[Any]=True	,  a						:Optional[Any]=0.0	,  a						:Tuple=0.0	,  a						:str=0.1	,  a						:Optional[Any]="gelu"	,  a						:str=False	,  a						:str=0.02	,  a						:List[Any]=1E-5	,  a						:int=None	,  a						:List[Any]=None	,  **a						:Optional[Any]	,  )						->					Optional[Any]:
								super().__init__(**a		)

								__UpperCamelCase      :  Any       =   image_size
								__UpperCamelCase      :  Tuple       =   patch_size
								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  List[Any]       =   embed_dim
								__UpperCamelCase      :  Union[str, Any]       =   depths
								__UpperCamelCase      :  Union[str, Any]       =   len(a		)
								__UpperCamelCase      :  Union[str, Any]       =   num_heads
								__UpperCamelCase      :  Optional[int]       =   window_size
								__UpperCamelCase      :  str       =   mlp_ratio
								__UpperCamelCase      :  List[str]       =   qkv_bias
								__UpperCamelCase      :  int       =   hidden_dropout_prob
								__UpperCamelCase      :  str       =   attention_probs_dropout_prob
								__UpperCamelCase      :  Dict       =   drop_path_rate
								__UpperCamelCase      :  Optional[Any]       =   hidden_act
								__UpperCamelCase      :  Union[str, Any]       =   use_absolute_embeddings
								__UpperCamelCase      :  List[str]       =   layer_norm_eps
								__UpperCamelCase      :  str       =   initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__UpperCamelCase      :  Any       =   int(embed_dim * 2 ** (len(a		) - 1)		)
								__UpperCamelCase      :  Optional[int]       =   ["stem"] + [f'stage{idx}' for idx in range(1	,  len(a		) + 1		)]
								__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   get_aligned_output_features_output_indices(
								    out_features=a	,  out_indices=a	,  stage_names=self.stage_names		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)